Overview

Dataset statistics

Number of variables20
Number of observations45593
Missing cells3762
Missing cells (%)0.4%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory7.0 MiB
Average record size in memory160.0 B

Variable types

Text3
Numeric6
DateTime2
Categorical9

Alerts

Delivery_location_latitude is highly overall correlated with Restaurant_latitudeHigh correlation
Delivery_location_longitude is highly overall correlated with Restaurant_longitudeHigh correlation
Restaurant_latitude is highly overall correlated with Delivery_location_latitudeHigh correlation
Restaurant_longitude is highly overall correlated with Delivery_location_longitudeHigh correlation
Road_traffic_density is highly overall correlated with Vehicle_conditionHigh correlation
Vehicle_condition is highly overall correlated with Road_traffic_density and 1 other fieldsHigh correlation
Weatherconditions is highly overall correlated with Vehicle_conditionHigh correlation
Festival is highly imbalanced (88.3%)Imbalance
City is highly imbalanced (51.8%)Imbalance
Delivery_person_Age has 1854 (4.1%) missing valuesMissing
Delivery_person_Ratings has 1908 (4.2%) missing valuesMissing
ID has unique valuesUnique
Restaurant_latitude has 3640 (8.0%) zerosZeros
Restaurant_longitude has 3640 (8.0%) zerosZeros

Reproduction

Analysis started2024-02-26 02:21:18.018011
Analysis finished2024-02-26 02:21:26.605441
Duration8.59 seconds
Software versionydata-profiling vv4.6.4
Download configurationconfig.json

Variables

ID
Text

UNIQUE 

Distinct45593
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size356.3 KiB
2024-02-26T09:21:26.932992image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Length

Max length7
Median length7
Mean length6.9457812
Min length6

Characters and Unicode

Total characters316679
Distinct characters18
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique45593 ?
Unique (%)100.0%

Sample

1st row0x4607
2nd row0xb379
3rd row0x5d6d
4th row0x7a6a
5th row0x70a2
ValueCountFrequency (%)
0x4607 1
 
< 0.1%
0x36b8 1
 
< 0.1%
0xb816 1
 
< 0.1%
0x6c6b 1
 
< 0.1%
0xd987 1
 
< 0.1%
0x5d6d 1
 
< 0.1%
0x7a6a 1
 
< 0.1%
0x70a2 1
 
< 0.1%
0x9bb4 1
 
< 0.1%
0x95b4 1
 
< 0.1%
Other values (45583) 45583
> 99.9%
2024-02-26T09:21:27.184464image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 54129
17.1%
x 45593
14.4%
45593
14.4%
b 11995
 
3.8%
4 11890
 
3.8%
7 11886
 
3.8%
6 11874
 
3.7%
a 11865
 
3.7%
1 11824
 
3.7%
c 11816
 
3.7%
Other values (8) 88214
27.9%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 160515
50.7%
Lowercase Letter 110571
34.9%
Space Separator 45593
 
14.4%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 54129
33.7%
4 11890
 
7.4%
7 11886
 
7.4%
6 11874
 
7.4%
1 11824
 
7.4%
5 11813
 
7.4%
2 11812
 
7.4%
8 11812
 
7.4%
9 11787
 
7.3%
3 11688
 
7.3%
Lowercase Letter
ValueCountFrequency (%)
x 45593
41.2%
b 11995
 
10.8%
a 11865
 
10.7%
c 11816
 
10.7%
d 11796
 
10.7%
e 8990
 
8.1%
f 8516
 
7.7%
Space Separator
ValueCountFrequency (%)
45593
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 206108
65.1%
Latin 110571
34.9%

Most frequent character per script

Common
ValueCountFrequency (%)
0 54129
26.3%
45593
22.1%
4 11890
 
5.8%
7 11886
 
5.8%
6 11874
 
5.8%
1 11824
 
5.7%
5 11813
 
5.7%
2 11812
 
5.7%
8 11812
 
5.7%
9 11787
 
5.7%
Latin
ValueCountFrequency (%)
x 45593
41.2%
b 11995
 
10.8%
a 11865
 
10.7%
c 11816
 
10.7%
d 11796
 
10.7%
e 8990
 
8.1%
f 8516
 
7.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII 316679
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 54129
17.1%
x 45593
14.4%
45593
14.4%
b 11995
 
3.8%
4 11890
 
3.8%
7 11886
 
3.8%
6 11874
 
3.7%
a 11865
 
3.7%
1 11824
 
3.7%
c 11816
 
3.7%
Other values (8) 88214
27.9%
Distinct1320
Distinct (%)2.9%
Missing0
Missing (%)0.0%
Memory size356.3 KiB
2024-02-26T09:21:27.306651image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Length

Max length18
Median length14
Mean length14.71035
Min length14

Characters and Unicode

Total characters670689
Distinct characters31
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowINDORES13DEL02
2nd rowBANGRES18DEL02
3rd rowBANGRES19DEL01
4th rowCOIMBRES13DEL02
5th rowCHENRES12DEL01
ValueCountFrequency (%)
puneres01del01 67
 
0.1%
japres11del02 67
 
0.1%
hydres04del02 66
 
0.1%
japres03del01 66
 
0.1%
vadres11del02 66
 
0.1%
ranchires02del01 66
 
0.1%
vadres08del02 66
 
0.1%
vadres11del01 65
 
0.1%
vadres14del01 65
 
0.1%
bangres03del01 65
 
0.1%
Other values (1310) 44934
98.6%
2024-02-26T09:21:27.501323image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
E 98200
14.6%
0 72942
10.9%
D 56594
 
8.4%
R 53475
 
8.0%
S 51951
 
7.7%
L 47791
 
7.1%
45593
 
6.8%
1 43745
 
6.5%
2 23301
 
3.5%
3 17335
 
2.6%
Other values (21) 159762
23.8%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 440456
65.7%
Decimal Number 184640
27.5%
Space Separator 45593
 
6.8%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
E 98200
22.3%
D 56594
12.8%
R 53475
12.1%
S 51951
11.8%
L 47791
10.9%
N 16600
 
3.8%
A 15948
 
3.6%
M 12687
 
2.9%
H 12481
 
2.8%
U 10953
 
2.5%
Other values (10) 63776
14.5%
Decimal Number
ValueCountFrequency (%)
0 72942
39.5%
1 43745
23.7%
2 23301
 
12.6%
3 17335
 
9.4%
5 4575
 
2.5%
6 4568
 
2.5%
7 4563
 
2.5%
4 4557
 
2.5%
9 4552
 
2.5%
8 4502
 
2.4%
Space Separator
ValueCountFrequency (%)
45593
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 440456
65.7%
Common 230233
34.3%

Most frequent character per script

Latin
ValueCountFrequency (%)
E 98200
22.3%
D 56594
12.8%
R 53475
12.1%
S 51951
11.8%
L 47791
10.9%
N 16600
 
3.8%
A 15948
 
3.6%
M 12687
 
2.9%
H 12481
 
2.8%
U 10953
 
2.5%
Other values (10) 63776
14.5%
Common
ValueCountFrequency (%)
0 72942
31.7%
45593
19.8%
1 43745
19.0%
2 23301
 
10.1%
3 17335
 
7.5%
5 4575
 
2.0%
6 4568
 
2.0%
7 4563
 
2.0%
4 4557
 
2.0%
9 4552
 
2.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 670689
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
E 98200
14.6%
0 72942
10.9%
D 56594
 
8.4%
R 53475
 
8.0%
S 51951
 
7.7%
L 47791
 
7.1%
45593
 
6.8%
1 43745
 
6.5%
2 23301
 
3.5%
3 17335
 
2.6%
Other values (21) 159762
23.8%

Delivery_person_Age
Real number (ℝ)

MISSING 

Distinct22
Distinct (%)0.1%
Missing1854
Missing (%)4.1%
Infinite0
Infinite (%)0.0%
Mean29.567137
Minimum15
Maximum50
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size356.3 KiB
2024-02-26T09:21:27.584917image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum15
5-th percentile21
Q125
median30
Q335
95-th percentile39
Maximum50
Range35
Interquartile range (IQR)10

Descriptive statistics

Standard deviation5.8151554
Coefficient of variation (CV)0.19667631
Kurtosis-1.0583326
Mean29.567137
Median Absolute Deviation (MAD)5
Skewness0.018669335
Sum1293237
Variance33.816032
MonotonicityNot monotonic
2024-02-26T09:21:27.657029image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=22)
ValueCountFrequency (%)
35 2262
 
5.0%
36 2260
 
5.0%
37 2227
 
4.9%
30 2226
 
4.9%
38 2219
 
4.9%
24 2210
 
4.8%
32 2202
 
4.8%
22 2196
 
4.8%
29 2191
 
4.8%
33 2187
 
4.8%
Other values (12) 21559
47.3%
ValueCountFrequency (%)
15 38
 
0.1%
20 2136
4.7%
21 2153
4.7%
22 2196
4.8%
23 2087
4.6%
24 2210
4.8%
25 2174
4.8%
26 2159
4.7%
27 2150
4.7%
28 2179
4.8%
ValueCountFrequency (%)
50 53
 
0.1%
39 2144
4.7%
38 2219
4.9%
37 2227
4.9%
36 2260
5.0%
35 2262
5.0%
34 2166
4.8%
33 2187
4.8%
32 2202
4.8%
31 2120
4.6%

Delivery_person_Ratings
Real number (ℝ)

MISSING 

Distinct28
Distinct (%)0.1%
Missing1908
Missing (%)4.2%
Infinite0
Infinite (%)0.0%
Mean4.6337805
Minimum1
Maximum6
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size356.3 KiB
2024-02-26T09:21:27.734234image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile4
Q14.5
median4.7
Q34.9
95-th percentile5
Maximum6
Range5
Interquartile range (IQR)0.4

Descriptive statistics

Standard deviation0.33471641
Coefficient of variation (CV)0.07223398
Kurtosis15.670705
Mean4.6337805
Median Absolute Deviation (MAD)0.2
Skewness-2.4935516
Sum202426.7
Variance0.11203507
MonotonicityNot monotonic
2024-02-26T09:21:27.812291image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=28)
ValueCountFrequency (%)
4.8 7148
15.7%
4.7 7142
15.7%
4.9 7041
15.4%
4.6 6940
15.2%
5 3996
8.8%
4.5 3303
7.2%
4.1 1430
 
3.1%
4.2 1418
 
3.1%
4.3 1409
 
3.1%
4.4 1361
 
3.0%
Other values (18) 2497
 
5.5%
(Missing) 1908
 
4.2%
ValueCountFrequency (%)
1 38
0.1%
2.5 20
< 0.1%
2.6 22
< 0.1%
2.7 22
< 0.1%
2.8 19
< 0.1%
2.9 19
< 0.1%
3 6
 
< 0.1%
3.1 29
0.1%
3.2 29
0.1%
3.3 25
0.1%
ValueCountFrequency (%)
6 53
 
0.1%
5 3996
8.8%
4.9 7041
15.4%
4.8 7148
15.7%
4.7 7142
15.7%
4.6 6940
15.2%
4.5 3303
7.2%
4.4 1361
 
3.0%
4.3 1409
 
3.1%
4.2 1418
 
3.1%

Restaurant_latitude
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct657
Distinct (%)1.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean17.017729
Minimum-30.905562
Maximum30.914057
Zeros3640
Zeros (%)8.0%
Negative431
Negative (%)0.9%
Memory size356.3 KiB
2024-02-26T09:21:27.890667image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum-30.905562
5-th percentile0
Q112.933284
median18.546947
Q322.728163
95-th percentile26.913987
Maximum30.914057
Range61.819619
Interquartile range (IQR)9.794879

Descriptive statistics

Standard deviation8.185109
Coefficient of variation (CV)0.48097541
Kurtosis3.713716
Mean17.017729
Median Absolute Deviation (MAD)5.482766
Skewness-1.3615831
Sum775889.3
Variance66.996009
MonotonicityNot monotonic
2024-02-26T09:21:27.973010image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 3640
 
8.0%
26.911378 182
 
0.4%
26.914142 180
 
0.4%
26.892312 176
 
0.4%
26.90294 176
 
0.4%
26.902908 176
 
0.4%
26.88842 174
 
0.4%
26.905287 173
 
0.4%
26.913726 173
 
0.4%
22.308096 172
 
0.4%
Other values (647) 40371
88.5%
ValueCountFrequency (%)
-30.905562 1
 
< 0.1%
-30.902872 2
< 0.1%
-30.899584 3
< 0.1%
-30.895817 3
< 0.1%
-30.893384 1
 
< 0.1%
-30.893244 1
 
< 0.1%
-30.892978 1
 
< 0.1%
-30.890184 1
 
< 0.1%
-30.885915 1
 
< 0.1%
-30.885814 1
 
< 0.1%
ValueCountFrequency (%)
30.914057 42
0.1%
30.905562 37
0.1%
30.902872 32
0.1%
30.899992 38
0.1%
30.899584 41
0.1%
30.895817 36
0.1%
30.895204 41
0.1%
30.893384 38
0.1%
30.893244 38
0.1%
30.893234 39
0.1%

Restaurant_longitude
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct518
Distinct (%)1.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean70.231332
Minimum-88.366217
Maximum88.433452
Zeros3640
Zeros (%)8.0%
Negative162
Negative (%)0.4%
Memory size356.3 KiB
2024-02-26T09:21:28.056494image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum-88.366217
5-th percentile0
Q173.17
median75.898497
Q378.044095
95-th percentile85.325347
Maximum88.433452
Range176.79967
Interquartile range (IQR)4.874095

Descriptive statistics

Standard deviation22.883647
Coefficient of variation (CV)0.32583245
Kurtosis10.303039
Mean70.231332
Median Absolute Deviation (MAD)2.161724
Skewness-3.2201594
Sum3202057.1
Variance523.66131
MonotonicityNot monotonic
2024-02-26T09:21:28.140213image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 3640
 
8.0%
75.789034 182
 
0.4%
75.805704 181
 
0.4%
75.793007 177
 
0.4%
75.806896 176
 
0.4%
75.792934 176
 
0.4%
75.75282 174
 
0.4%
75.800689 174
 
0.4%
75.794592 173
 
0.4%
73.167753 173
 
0.4%
Other values (508) 40367
88.5%
ValueCountFrequency (%)
-88.366217 1
 
< 0.1%
-88.352885 1
 
< 0.1%
-88.349843 1
 
< 0.1%
-88.322337 1
 
< 0.1%
-85.33982 1
 
< 0.1%
-85.335486 1
 
< 0.1%
-85.325731 3
< 0.1%
-85.325447 2
< 0.1%
-85.325146 1
 
< 0.1%
-85.3172 1
 
< 0.1%
ValueCountFrequency (%)
88.433452 35
0.1%
88.433187 36
0.1%
88.400581 34
0.1%
88.400467 33
0.1%
88.39331 36
0.1%
88.393294 38
0.1%
88.368628 35
0.1%
88.36783 33
0.1%
88.366217 33
0.1%
88.365507 37
0.1%

Delivery_location_latitude
Real number (ℝ)

HIGH CORRELATION 

Distinct4373
Distinct (%)9.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean17.465186
Minimum0.01
Maximum31.054057
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size356.3 KiB
2024-02-26T09:21:28.230243image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum0.01
5-th percentile0.07
Q112.988453
median18.633934
Q322.785049
95-th percentile27.023726
Maximum31.054057
Range31.044057
Interquartile range (IQR)9.796596

Descriptive statistics

Standard deviation7.335122
Coefficient of variation (CV)0.41998534
Kurtosis0.26434584
Mean17.465186
Median Absolute Deviation (MAD)5.47924
Skewness-0.70106646
Sum796290.22
Variance53.804015
MonotonicityNot monotonic
2024-02-26T09:21:28.315698image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.13 341
 
0.7%
0.02 337
 
0.7%
0.09 336
 
0.7%
0.06 336
 
0.7%
0.07 335
 
0.7%
0.04 335
 
0.7%
0.05 328
 
0.7%
0.11 328
 
0.7%
0.01 327
 
0.7%
0.08 324
 
0.7%
Other values (4363) 42266
92.7%
ValueCountFrequency (%)
0.01 327
0.7%
0.02 337
0.7%
0.03 313
0.7%
0.04 335
0.7%
0.05 328
0.7%
0.06 336
0.7%
0.07 335
0.7%
0.08 324
0.7%
0.09 336
0.7%
0.11 328
0.7%
ValueCountFrequency (%)
31.054057 3
< 0.1%
31.045562 4
< 0.1%
31.044057 4
< 0.1%
31.042872 2
< 0.1%
31.039992 3
< 0.1%
31.039584 4
< 0.1%
31.035817 4
< 0.1%
31.035562 3
< 0.1%
31.035204 4
< 0.1%
31.033384 4
< 0.1%

Delivery_location_longitude
Real number (ℝ)

HIGH CORRELATION 

Distinct4373
Distinct (%)9.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean70.845702
Minimum0.01
Maximum88.563452
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size356.3 KiB
2024-02-26T09:21:28.403849image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum0.01
5-th percentile0.07
Q173.28
median76.002574
Q378.107044
95-th percentile85.375486
Maximum88.563452
Range88.553452
Interquartile range (IQR)4.827044

Descriptive statistics

Standard deviation21.118812
Coefficient of variation (CV)0.29809588
Kurtosis7.1044509
Mean70.845702
Median Absolute Deviation (MAD)2.196673
Skewness-2.9563849
Sum3230068.1
Variance446.00422
MonotonicityNot monotonic
2024-02-26T09:21:28.492552image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.13 341
 
0.7%
0.02 337
 
0.7%
0.09 336
 
0.7%
0.06 336
 
0.7%
0.07 335
 
0.7%
0.04 335
 
0.7%
0.05 328
 
0.7%
0.11 328
 
0.7%
0.01 327
 
0.7%
0.08 324
 
0.7%
Other values (4363) 42266
92.7%
ValueCountFrequency (%)
0.01 327
0.7%
0.02 337
0.7%
0.03 313
0.7%
0.04 335
0.7%
0.05 328
0.7%
0.06 336
0.7%
0.07 335
0.7%
0.08 324
0.7%
0.09 336
0.7%
0.11 328
0.7%
ValueCountFrequency (%)
88.563452 2
< 0.1%
88.563187 4
< 0.1%
88.543452 3
< 0.1%
88.543187 4
< 0.1%
88.530581 4
< 0.1%
88.530467 3
< 0.1%
88.523452 4
< 0.1%
88.52331 4
< 0.1%
88.523294 2
< 0.1%
88.523187 2
< 0.1%
Distinct44
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size356.3 KiB
Minimum2022-01-03 00:00:00
Maximum2022-12-03 00:00:00
2024-02-26T09:21:28.571190image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-02-26T09:21:28.661390image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=44)
Distinct177
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Memory size356.3 KiB
2024-02-26T09:21:28.773823image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Length

Max length8
Median length8
Mean length7.8481346
Min length4

Characters and Unicode

Total characters357820
Distinct characters14
Distinct categories5 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row11:30:00
2nd row19:45:00
3rd row08:30:00
4th row18:00:00
5th row13:30:00
ValueCountFrequency (%)
nan 1731
 
3.8%
21:55:00 461
 
1.0%
17:55:00 456
 
1.0%
20:00:00 449
 
1.0%
22:20:00 448
 
1.0%
21:35:00 446
 
1.0%
19:50:00 444
 
1.0%
21:15:00 442
 
1.0%
22:45:00 438
 
1.0%
21:20:00 438
 
1.0%
Other values (167) 39840
87.4%
2024-02-26T09:21:28.980140image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 126859
35.5%
: 87724
24.5%
1 35839
 
10.0%
2 31750
 
8.9%
5 28784
 
8.0%
3 13300
 
3.7%
4 8813
 
2.5%
9 6542
 
1.8%
8 6298
 
1.8%
7 4278
 
1.2%
Other values (4) 7633
 
2.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 263172
73.5%
Other Punctuation 87724
 
24.5%
Uppercase Letter 3462
 
1.0%
Lowercase Letter 1731
 
0.5%
Space Separator 1731
 
0.5%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 126859
48.2%
1 35839
 
13.6%
2 31750
 
12.1%
5 28784
 
10.9%
3 13300
 
5.1%
4 8813
 
3.3%
9 6542
 
2.5%
8 6298
 
2.4%
7 4278
 
1.6%
6 709
 
0.3%
Other Punctuation
ValueCountFrequency (%)
: 87724
100.0%
Uppercase Letter
ValueCountFrequency (%)
N 3462
100.0%
Lowercase Letter
ValueCountFrequency (%)
a 1731
100.0%
Space Separator
ValueCountFrequency (%)
1731
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 352627
98.5%
Latin 5193
 
1.5%

Most frequent character per script

Common
ValueCountFrequency (%)
0 126859
36.0%
: 87724
24.9%
1 35839
 
10.2%
2 31750
 
9.0%
5 28784
 
8.2%
3 13300
 
3.8%
4 8813
 
2.5%
9 6542
 
1.9%
8 6298
 
1.8%
7 4278
 
1.2%
Other values (2) 2440
 
0.7%
Latin
ValueCountFrequency (%)
N 3462
66.7%
a 1731
33.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 357820
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 126859
35.5%
: 87724
24.5%
1 35839
 
10.0%
2 31750
 
8.9%
5 28784
 
8.0%
3 13300
 
3.7%
4 8813
 
2.5%
9 6542
 
1.8%
8 6298
 
1.8%
7 4278
 
1.2%
Other values (4) 7633
 
2.1%
Distinct193
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Memory size356.3 KiB
Minimum2024-02-26 00:00:00
Maximum2024-02-26 23:55:00
2024-02-26T09:21:29.075732image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-02-26T09:21:29.153687image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

Weatherconditions
Categorical

HIGH CORRELATION 

Distinct7
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size356.3 KiB
conditions Fog
7654 
conditions Stormy
7586 
conditions Cloudy
7536 
conditions Sandstorms
7495 
conditions Windy
7422 
Other values (2)
7900 

Length

Max length21
Median length17
Mean length16.790845
Min length14

Characters and Unicode

Total characters765545
Distinct characters20
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowconditions Sunny
2nd rowconditions Stormy
3rd rowconditions Sandstorms
4th rowconditions Sunny
5th rowconditions Cloudy

Common Values

ValueCountFrequency (%)
conditions Fog 7654
16.8%
conditions Stormy 7586
16.6%
conditions Cloudy 7536
16.5%
conditions Sandstorms 7495
16.4%
conditions Windy 7422
16.3%
conditions Sunny 7284
16.0%
conditions NaN 616
 
1.4%

Length

2024-02-26T09:21:29.233441image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-02-26T09:21:29.313515image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
ValueCountFrequency (%)
conditions 45593
50.0%
fog 7654
 
8.4%
stormy 7586
 
8.3%
cloudy 7536
 
8.3%
sandstorms 7495
 
8.2%
windy 7422
 
8.1%
sunny 7284
 
8.0%
nan 616
 
0.7%

Most occurring characters

ValueCountFrequency (%)
o 121457
15.9%
n 120671
15.8%
i 98608
12.9%
d 68046
8.9%
t 60674
7.9%
s 60583
7.9%
c 45593
 
6.0%
45593
 
6.0%
y 29828
 
3.9%
S 22365
 
2.9%
Other values (10) 92127
12.0%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 673743
88.0%
Uppercase Letter 46209
 
6.0%
Space Separator 45593
 
6.0%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
o 121457
18.0%
n 120671
17.9%
i 98608
14.6%
d 68046
10.1%
t 60674
9.0%
s 60583
9.0%
c 45593
 
6.8%
y 29828
 
4.4%
r 15081
 
2.2%
m 15081
 
2.2%
Other values (4) 38121
 
5.7%
Uppercase Letter
ValueCountFrequency (%)
S 22365
48.4%
F 7654
 
16.6%
C 7536
 
16.3%
W 7422
 
16.1%
N 1232
 
2.7%
Space Separator
ValueCountFrequency (%)
45593
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 719952
94.0%
Common 45593
 
6.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
o 121457
16.9%
n 120671
16.8%
i 98608
13.7%
d 68046
9.5%
t 60674
8.4%
s 60583
8.4%
c 45593
 
6.3%
y 29828
 
4.1%
S 22365
 
3.1%
r 15081
 
2.1%
Other values (9) 77046
10.7%
Common
ValueCountFrequency (%)
45593
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 765545
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
o 121457
15.9%
n 120671
15.8%
i 98608
12.9%
d 68046
8.9%
t 60674
7.9%
s 60583
7.9%
c 45593
 
6.0%
45593
 
6.0%
y 29828
 
3.9%
S 22365
 
2.9%
Other values (10) 92127
12.0%

Road_traffic_density
Categorical

HIGH CORRELATION 

Distinct5
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size356.3 KiB
Low
15477 
Jam
14143 
Medium
10947 
High
4425 
NaN
 
601

Length

Max length7
Median length4
Mean length4.8173623
Min length4

Characters and Unicode

Total characters219638
Distinct characters16
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowHigh
2nd rowJam
3rd rowLow
4th rowMedium
5th rowHigh

Common Values

ValueCountFrequency (%)
Low 15477
33.9%
Jam 14143
31.0%
Medium 10947
24.0%
High 4425
 
9.7%
NaN 601
 
1.3%

Length

2024-02-26T09:21:29.382878image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-02-26T09:21:29.466300image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
ValueCountFrequency (%)
low 15477
33.9%
jam 14143
31.0%
medium 10947
24.0%
high 4425
 
9.7%
nan 601
 
1.3%

Most occurring characters

ValueCountFrequency (%)
45593
20.8%
m 25090
11.4%
L 15477
 
7.0%
o 15477
 
7.0%
w 15477
 
7.0%
i 15372
 
7.0%
a 14744
 
6.7%
J 14143
 
6.4%
M 10947
 
5.0%
e 10947
 
5.0%
Other values (6) 36371
16.6%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 127851
58.2%
Uppercase Letter 46194
 
21.0%
Space Separator 45593
 
20.8%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
m 25090
19.6%
o 15477
12.1%
w 15477
12.1%
i 15372
12.0%
a 14744
11.5%
e 10947
8.6%
d 10947
8.6%
u 10947
8.6%
g 4425
 
3.5%
h 4425
 
3.5%
Uppercase Letter
ValueCountFrequency (%)
L 15477
33.5%
J 14143
30.6%
M 10947
23.7%
H 4425
 
9.6%
N 1202
 
2.6%
Space Separator
ValueCountFrequency (%)
45593
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 174045
79.2%
Common 45593
 
20.8%

Most frequent character per script

Latin
ValueCountFrequency (%)
m 25090
14.4%
L 15477
8.9%
o 15477
8.9%
w 15477
8.9%
i 15372
8.8%
a 14744
8.5%
J 14143
8.1%
M 10947
6.3%
e 10947
6.3%
d 10947
6.3%
Other values (5) 25424
14.6%
Common
ValueCountFrequency (%)
45593
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 219638
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
45593
20.8%
m 25090
11.4%
L 15477
 
7.0%
o 15477
 
7.0%
w 15477
 
7.0%
i 15372
 
7.0%
a 14744
 
6.7%
J 14143
 
6.4%
M 10947
 
5.0%
e 10947
 
5.0%
Other values (6) 36371
16.6%

Vehicle_condition
Categorical

HIGH CORRELATION 

Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size356.3 KiB
2
15034 
1
15030 
0
15009 
3
 
520

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters45593
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2
2nd row2
3rd row0
4th row0
5th row1

Common Values

ValueCountFrequency (%)
2 15034
33.0%
1 15030
33.0%
0 15009
32.9%
3 520
 
1.1%

Length

2024-02-26T09:21:29.528767image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-02-26T09:21:29.604806image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
ValueCountFrequency (%)
2 15034
33.0%
1 15030
33.0%
0 15009
32.9%
3 520
 
1.1%

Most occurring characters

ValueCountFrequency (%)
2 15034
33.0%
1 15030
33.0%
0 15009
32.9%
3 520
 
1.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 45593
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
2 15034
33.0%
1 15030
33.0%
0 15009
32.9%
3 520
 
1.1%

Most occurring scripts

ValueCountFrequency (%)
Common 45593
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
2 15034
33.0%
1 15030
33.0%
0 15009
32.9%
3 520
 
1.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 45593
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
2 15034
33.0%
1 15030
33.0%
0 15009
32.9%
3 520
 
1.1%

Type_of_order
Categorical

Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size356.3 KiB
Snack
11533 
Meal
11458 
Drinks
11322 
Buffet
11280 

Length

Max length7
Median length6
Mean length6.2444235
Min length5

Characters and Unicode

Total characters284702
Distinct characters17
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowSnack
2nd rowSnack
3rd rowDrinks
4th rowBuffet
5th rowSnack

Common Values

ValueCountFrequency (%)
Snack 11533
25.3%
Meal 11458
25.1%
Drinks 11322
24.8%
Buffet 11280
24.7%

Length

2024-02-26T09:21:29.667325image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-02-26T09:21:29.750851image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
ValueCountFrequency (%)
snack 11533
25.3%
meal 11458
25.1%
drinks 11322
24.8%
buffet 11280
24.7%

Most occurring characters

ValueCountFrequency (%)
45593
16.0%
a 22991
 
8.1%
n 22855
 
8.0%
k 22855
 
8.0%
e 22738
 
8.0%
f 22560
 
7.9%
S 11533
 
4.1%
c 11533
 
4.1%
l 11458
 
4.0%
M 11458
 
4.0%
Other values (7) 79128
27.8%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 193516
68.0%
Space Separator 45593
 
16.0%
Uppercase Letter 45593
 
16.0%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
a 22991
11.9%
n 22855
11.8%
k 22855
11.8%
e 22738
11.7%
f 22560
11.7%
c 11533
6.0%
l 11458
5.9%
r 11322
5.9%
i 11322
5.9%
s 11322
5.9%
Other values (2) 22560
11.7%
Uppercase Letter
ValueCountFrequency (%)
S 11533
25.3%
M 11458
25.1%
D 11322
24.8%
B 11280
24.7%
Space Separator
ValueCountFrequency (%)
45593
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 239109
84.0%
Common 45593
 
16.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
a 22991
 
9.6%
n 22855
 
9.6%
k 22855
 
9.6%
e 22738
 
9.5%
f 22560
 
9.4%
S 11533
 
4.8%
c 11533
 
4.8%
l 11458
 
4.8%
M 11458
 
4.8%
D 11322
 
4.7%
Other values (6) 67806
28.4%
Common
ValueCountFrequency (%)
45593
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 284702
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
45593
16.0%
a 22991
 
8.1%
n 22855
 
8.0%
k 22855
 
8.0%
e 22738
 
8.0%
f 22560
 
7.9%
S 11533
 
4.1%
c 11533
 
4.1%
l 11458
 
4.0%
M 11458
 
4.0%
Other values (7) 79128
27.8%

Type_of_vehicle
Categorical

Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size356.3 KiB
motorcycle
26435 
scooter
15276 
electric_scooter
3814 
bicycle
 
68

Length

Max length17
Median length11
Mean length10.49229
Min length8

Characters and Unicode

Total characters478375
Distinct characters13
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowmotorcycle
2nd rowscooter
3rd rowmotorcycle
4th rowmotorcycle
5th rowscooter

Common Values

ValueCountFrequency (%)
motorcycle 26435
58.0%
scooter 15276
33.5%
electric_scooter 3814
 
8.4%
bicycle 68
 
0.1%

Length

2024-02-26T09:21:29.813592image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-02-26T09:21:29.882735image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
ValueCountFrequency (%)
motorcycle 26435
58.0%
scooter 15276
33.5%
electric_scooter 3814
 
8.4%
bicycle 68
 
0.1%

Most occurring characters

ValueCountFrequency (%)
o 91050
19.0%
c 79724
16.7%
e 53221
11.1%
t 49339
10.3%
r 49339
10.3%
45593
9.5%
l 30317
 
6.3%
y 26503
 
5.5%
m 26435
 
5.5%
s 19090
 
4.0%
Other values (3) 7764
 
1.6%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 428968
89.7%
Space Separator 45593
 
9.5%
Connector Punctuation 3814
 
0.8%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
o 91050
21.2%
c 79724
18.6%
e 53221
12.4%
t 49339
11.5%
r 49339
11.5%
l 30317
 
7.1%
y 26503
 
6.2%
m 26435
 
6.2%
s 19090
 
4.5%
i 3882
 
0.9%
Space Separator
ValueCountFrequency (%)
45593
100.0%
Connector Punctuation
ValueCountFrequency (%)
_ 3814
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 428968
89.7%
Common 49407
 
10.3%

Most frequent character per script

Latin
ValueCountFrequency (%)
o 91050
21.2%
c 79724
18.6%
e 53221
12.4%
t 49339
11.5%
r 49339
11.5%
l 30317
 
7.1%
y 26503
 
6.2%
m 26435
 
6.2%
s 19090
 
4.5%
i 3882
 
0.9%
Common
ValueCountFrequency (%)
45593
92.3%
_ 3814
 
7.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII 478375
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
o 91050
19.0%
c 79724
16.7%
e 53221
11.1%
t 49339
10.3%
r 49339
10.3%
45593
9.5%
l 30317
 
6.3%
y 26503
 
5.5%
m 26435
 
5.5%
s 19090
 
4.0%
Other values (3) 7764
 
1.6%
Distinct5
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size356.3 KiB
1
28159 
0
14095 
2
 
1985
NaN
 
993
3
 
361

Length

Max length4
Median length1
Mean length1.065339
Min length1

Characters and Unicode

Total characters48572
Distinct characters7
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row1
3rd row1
4th row1
5th row1

Common Values

ValueCountFrequency (%)
1 28159
61.8%
0 14095
30.9%
2 1985
 
4.4%
NaN 993
 
2.2%
3 361
 
0.8%

Length

2024-02-26T09:21:29.938720image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-02-26T09:21:30.015282image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
ValueCountFrequency (%)
1 28159
61.8%
0 14095
30.9%
2 1985
 
4.4%
nan 993
 
2.2%
3 361
 
0.8%

Most occurring characters

ValueCountFrequency (%)
1 28159
58.0%
0 14095
29.0%
N 1986
 
4.1%
2 1985
 
4.1%
a 993
 
2.0%
993
 
2.0%
3 361
 
0.7%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 44600
91.8%
Uppercase Letter 1986
 
4.1%
Lowercase Letter 993
 
2.0%
Space Separator 993
 
2.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 28159
63.1%
0 14095
31.6%
2 1985
 
4.5%
3 361
 
0.8%
Uppercase Letter
ValueCountFrequency (%)
N 1986
100.0%
Lowercase Letter
ValueCountFrequency (%)
a 993
100.0%
Space Separator
ValueCountFrequency (%)
993
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 45593
93.9%
Latin 2979
 
6.1%

Most frequent character per script

Common
ValueCountFrequency (%)
1 28159
61.8%
0 14095
30.9%
2 1985
 
4.4%
993
 
2.2%
3 361
 
0.8%
Latin
ValueCountFrequency (%)
N 1986
66.7%
a 993
33.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 48572
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 28159
58.0%
0 14095
29.0%
N 1986
 
4.1%
2 1985
 
4.1%
a 993
 
2.0%
993
 
2.0%
3 361
 
0.7%

Festival
Categorical

IMBALANCE 

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size356.3 KiB
No
44469 
Yes
 
896
NaN
 
228

Length

Max length4
Median length3
Mean length3.0246529
Min length3

Characters and Unicode

Total characters137903
Distinct characters7
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowNo
2nd rowNo
3rd rowNo
4th rowNo
5th rowNo

Common Values

ValueCountFrequency (%)
No 44469
97.5%
Yes 896
 
2.0%
NaN 228
 
0.5%

Length

2024-02-26T09:21:30.070597image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-02-26T09:21:30.133028image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
ValueCountFrequency (%)
no 44469
97.5%
yes 896
 
2.0%
nan 228
 
0.5%

Most occurring characters

ValueCountFrequency (%)
45593
33.1%
N 44925
32.6%
o 44469
32.2%
Y 896
 
0.6%
e 896
 
0.6%
s 896
 
0.6%
a 228
 
0.2%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 46489
33.7%
Uppercase Letter 45821
33.2%
Space Separator 45593
33.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
o 44469
95.7%
e 896
 
1.9%
s 896
 
1.9%
a 228
 
0.5%
Uppercase Letter
ValueCountFrequency (%)
N 44925
98.0%
Y 896
 
2.0%
Space Separator
ValueCountFrequency (%)
45593
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 92310
66.9%
Common 45593
33.1%

Most frequent character per script

Latin
ValueCountFrequency (%)
N 44925
48.7%
o 44469
48.2%
Y 896
 
1.0%
e 896
 
1.0%
s 896
 
1.0%
a 228
 
0.2%
Common
ValueCountFrequency (%)
45593
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 137903
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
45593
33.1%
N 44925
32.6%
o 44469
32.2%
Y 896
 
0.6%
e 896
 
0.6%
s 896
 
0.6%
a 228
 
0.2%

City
Categorical

IMBALANCE 

Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size356.3 KiB
Metropolitian
34093 
Urban
10136 
NaN
 
1200
Semi-Urban
 
164

Length

Max length14
Median length14
Mean length11.947492
Min length4

Characters and Unicode

Total characters544722
Distinct characters17
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowUrban
2nd rowMetropolitian
3rd rowUrban
4th rowMetropolitian
5th rowMetropolitian

Common Values

ValueCountFrequency (%)
Metropolitian 34093
74.8%
Urban 10136
 
22.2%
NaN 1200
 
2.6%
Semi-Urban 164
 
0.4%

Length

2024-02-26T09:21:30.195818image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-02-26T09:21:30.265137image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
ValueCountFrequency (%)
metropolitian 34093
74.8%
urban 10136
 
22.2%
nan 1200
 
2.6%
semi-urban 164
 
0.4%

Most occurring characters

ValueCountFrequency (%)
i 68350
12.5%
t 68186
12.5%
o 68186
12.5%
a 45593
8.4%
45593
8.4%
r 44393
8.1%
n 44393
8.1%
e 34257
6.3%
M 34093
6.3%
l 34093
6.3%
Other values (7) 57585
10.6%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 452008
83.0%
Uppercase Letter 46957
 
8.6%
Space Separator 45593
 
8.4%
Dash Punctuation 164
 
< 0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
i 68350
15.1%
t 68186
15.1%
o 68186
15.1%
a 45593
10.1%
r 44393
9.8%
n 44393
9.8%
e 34257
7.6%
l 34093
7.5%
p 34093
7.5%
b 10300
 
2.3%
Uppercase Letter
ValueCountFrequency (%)
M 34093
72.6%
U 10300
 
21.9%
N 2400
 
5.1%
S 164
 
0.3%
Space Separator
ValueCountFrequency (%)
45593
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 164
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 498965
91.6%
Common 45757
 
8.4%

Most frequent character per script

Latin
ValueCountFrequency (%)
i 68350
13.7%
t 68186
13.7%
o 68186
13.7%
a 45593
9.1%
r 44393
8.9%
n 44393
8.9%
e 34257
6.9%
M 34093
6.8%
l 34093
6.8%
p 34093
6.8%
Other values (5) 23328
 
4.7%
Common
ValueCountFrequency (%)
45593
99.6%
- 164
 
0.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII 544722
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
i 68350
12.5%
t 68186
12.5%
o 68186
12.5%
a 45593
8.4%
45593
8.4%
r 44393
8.1%
n 44393
8.1%
e 34257
6.3%
M 34093
6.3%
l 34093
6.3%
Other values (7) 57585
10.6%

Time_taken(min)
Categorical

Distinct45
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size356.3 KiB
(min) 26
 
2123
(min) 25
 
2050
(min) 27
 
1976
(min) 28
 
1965
(min) 29
 
1956
Other values (40)
35523 

Length

Max length8
Median length8
Mean length8
Min length8

Characters and Unicode

Total characters364744
Distinct characters16
Distinct categories5 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row(min) 24
2nd row(min) 33
3rd row(min) 26
4th row(min) 21
5th row(min) 30

Common Values

ValueCountFrequency (%)
(min) 26 2123
 
4.7%
(min) 25 2050
 
4.5%
(min) 27 1976
 
4.3%
(min) 28 1965
 
4.3%
(min) 29 1956
 
4.3%
(min) 19 1824
 
4.0%
(min) 15 1810
 
4.0%
(min) 18 1765
 
3.9%
(min) 16 1706
 
3.7%
(min) 17 1696
 
3.7%
Other values (35) 26722
58.6%

Length

2024-02-26T09:21:30.324113image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
min 45593
50.0%
26 2123
 
2.3%
25 2050
 
2.2%
27 1976
 
2.2%
28 1965
 
2.2%
29 1956
 
2.1%
19 1824
 
2.0%
15 1810
 
2.0%
18 1765
 
1.9%
16 1706
 
1.9%
Other values (36) 28418
31.2%

Most occurring characters

ValueCountFrequency (%)
( 45593
12.5%
m 45593
12.5%
i 45593
12.5%
n 45593
12.5%
) 45593
12.5%
45593
12.5%
2 22396
6.1%
1 16727
 
4.6%
3 14517
 
4.0%
4 8391
 
2.3%
Other values (6) 29155
8.0%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 136779
37.5%
Decimal Number 91186
25.0%
Open Punctuation 45593
 
12.5%
Close Punctuation 45593
 
12.5%
Space Separator 45593
 
12.5%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
2 22396
24.6%
1 16727
18.3%
3 14517
15.9%
4 8391
 
9.2%
5 5369
 
5.9%
6 4955
 
5.4%
9 4907
 
5.4%
8 4894
 
5.4%
7 4795
 
5.3%
0 4235
 
4.6%
Lowercase Letter
ValueCountFrequency (%)
m 45593
33.3%
i 45593
33.3%
n 45593
33.3%
Open Punctuation
ValueCountFrequency (%)
( 45593
100.0%
Close Punctuation
ValueCountFrequency (%)
) 45593
100.0%
Space Separator
ValueCountFrequency (%)
45593
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 227965
62.5%
Latin 136779
37.5%

Most frequent character per script

Common
ValueCountFrequency (%)
( 45593
20.0%
) 45593
20.0%
45593
20.0%
2 22396
9.8%
1 16727
 
7.3%
3 14517
 
6.4%
4 8391
 
3.7%
5 5369
 
2.4%
6 4955
 
2.2%
9 4907
 
2.2%
Other values (3) 13924
 
6.1%
Latin
ValueCountFrequency (%)
m 45593
33.3%
i 45593
33.3%
n 45593
33.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 364744
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
( 45593
12.5%
m 45593
12.5%
i 45593
12.5%
n 45593
12.5%
) 45593
12.5%
45593
12.5%
2 22396
6.1%
1 16727
 
4.6%
3 14517
 
4.0%
4 8391
 
2.3%
Other values (6) 29155
8.0%

Interactions

2024-02-26T09:21:25.572213image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-02-26T09:21:22.959670image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-02-26T09:21:23.619159image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-02-26T09:21:24.168482image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-02-26T09:21:24.633146image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-02-26T09:21:25.112276image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-02-26T09:21:25.649822image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-02-26T09:21:23.051279image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-02-26T09:21:23.709427image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-02-26T09:21:24.244285image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-02-26T09:21:24.722101image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-02-26T09:21:25.189912image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-02-26T09:21:25.721578image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-02-26T09:21:23.258158image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-02-26T09:21:23.799690image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-02-26T09:21:24.320529image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-02-26T09:21:24.800647image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-02-26T09:21:25.262000image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-02-26T09:21:25.796501image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-02-26T09:21:23.348442image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-02-26T09:21:23.889912image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-02-26T09:21:24.399088image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-02-26T09:21:24.880852image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-02-26T09:21:25.338099image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-02-26T09:21:25.870335image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-02-26T09:21:23.439891image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-02-26T09:21:23.988731image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-02-26T09:21:24.474968image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-02-26T09:21:24.954768image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-02-26T09:21:25.411619image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-02-26T09:21:25.943829image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-02-26T09:21:23.528943image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-02-26T09:21:24.082283image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-02-26T09:21:24.557718image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-02-26T09:21:25.030979image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-02-26T09:21:25.487184image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Correlations

2024-02-26T09:21:30.383120image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
CityDelivery_location_latitudeDelivery_location_longitudeDelivery_person_AgeDelivery_person_RatingsFestivalRestaurant_latitudeRestaurant_longitudeRoad_traffic_densityTime_taken(min)Type_of_orderType_of_vehicleVehicle_conditionWeatherconditionsmultiple_deliveries
City1.000-0.008-0.012-0.0620.0390.080-0.006-0.0080.0640.2400.0080.0320.0520.0320.106
Delivery_location_latitude-0.0081.0000.1220.005-0.0080.0000.9730.1160.0160.0110.0000.0110.0000.0000.007
Delivery_location_longitude-0.0120.1221.0000.008-0.0050.0000.1110.9880.0000.0050.0000.0090.0000.0000.004
Delivery_person_Age-0.0620.0050.0081.0000.0330.063-0.013-0.0030.3060.0800.0050.1460.3460.2520.067
Delivery_person_Ratings0.039-0.008-0.0050.0331.0000.060-0.022-0.0120.3160.1100.0030.1590.3600.2650.094
Festival0.0800.0000.0000.0630.0601.0000.003-0.0040.0910.3660.0000.0410.0750.0570.152
Restaurant_latitude-0.0060.9730.111-0.013-0.0220.0031.0000.1220.1450.0000.0000.0650.1640.1180.008
Restaurant_longitude-0.0080.1160.988-0.003-0.012-0.0040.1221.0000.2530.0150.0000.1090.2510.2550.000
Road_traffic_density0.0640.0160.0000.3060.3160.0910.1450.2531.0000.2370.0000.2030.5370.4940.093
Time_taken(min)0.2400.0110.0050.0800.1100.3660.0000.0150.2371.0000.0000.1100.1920.1320.300
Type_of_order0.0080.0000.0000.0050.0030.0000.0000.0000.0000.0001.0000.0000.0030.0000.006
Type_of_vehicle0.0320.0110.0090.1460.1590.0410.0650.1090.2030.1100.0001.0000.4570.2000.047
Vehicle_condition0.0520.0000.0000.3460.3600.0750.1640.2510.5370.1920.0030.4571.0000.5300.075
Weatherconditions0.0320.0000.0000.2520.2650.0570.1180.2550.4940.1320.0000.2000.5301.0000.058
multiple_deliveries0.1060.0070.0040.0670.0940.1520.0080.0000.0930.3000.0060.0470.0750.0581.000

Missing values

2024-02-26T09:21:26.099659image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-02-26T09:21:26.376418image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

IDDelivery_person_IDDelivery_person_AgeDelivery_person_RatingsRestaurant_latitudeRestaurant_longitudeDelivery_location_latitudeDelivery_location_longitudeOrder_DateTime_OrderdTime_Order_pickedWeatherconditionsRoad_traffic_densityVehicle_conditionType_of_orderType_of_vehiclemultiple_deliveriesFestivalCityTime_taken(min)
00x4607INDORES13DEL02374.922.74504975.89247122.76504975.91247119-03-202211:30:0011:45:00conditions SunnyHigh2Snackmotorcycle0NoUrban(min) 24
10xb379BANGRES18DEL02344.512.91304177.68323713.04304177.81323725-03-202219:45:0019:50:00conditions StormyJam2Snackscooter1NoMetropolitian(min) 33
20x5d6dBANGRES19DEL01234.412.91426477.67840012.92426477.68840019-03-202208:30:0008:45:00conditions SandstormsLow0Drinksmotorcycle1NoUrban(min) 26
30x7a6aCOIMBRES13DEL02384.711.00366976.97649411.05366977.02649405-04-202218:00:0018:10:00conditions SunnyMedium0Buffetmotorcycle1NoMetropolitian(min) 21
40x70a2CHENRES12DEL01324.612.97279380.24998213.01279380.28998226-03-202213:30:0013:45:00conditions CloudyHigh1Snackscooter1NoMetropolitian(min) 30
50x9bb4HYDRES09DEL03224.817.43166878.40832117.46166878.43832111-03-202221:20:0021:30:00conditions CloudyJam0Buffetmotorcycle1NoUrban(min) 26
60x95b4RANCHIRES15DEL01334.723.36974685.33982023.47974685.44982004-03-202219:15:0019:30:00conditions FogJam1Mealscooter1NoMetropolitian(min) 40
70x9eb2MYSRES15DEL02354.612.35205876.60665012.48205876.73665014-03-202217:25:0017:30:00conditions CloudyMedium2Mealmotorcycle1NoMetropolitian(min) 32
80x1102HYDRES05DEL02224.817.43380978.38674417.56380978.51674420-03-202220:55:0021:05:00conditions StormyJam0Buffetmotorcycle1NoMetropolitian(min) 34
90xcdcdDEHRES17DEL01364.230.32796878.04610630.39796878.11610612-02-202221:55:0022:10:00conditions FogJam2Snackmotorcycle3NoMetropolitian(min) 46
IDDelivery_person_IDDelivery_person_AgeDelivery_person_RatingsRestaurant_latitudeRestaurant_longitudeDelivery_location_latitudeDelivery_location_longitudeOrder_DateTime_OrderdTime_Order_pickedWeatherconditionsRoad_traffic_densityVehicle_conditionType_of_orderType_of_vehiclemultiple_deliveriesFestivalCityTime_taken(min)
455830x5193MYSRES13DEL02364.812.31097276.65926412.44097276.78926418-03-202221:10:0021:20:00conditions SunnyJam2Drinkselectric_scooter1NoUrban(min) 29
455840xa333CHENRES08DEL02374.813.02239480.24243913.04239480.26243905-04-202209:35:0009:50:00conditions SandstormsLow2Drinkselectric_scooter0NoMetropolitian(min) 20
455850xc9abKNPRES03DEL01304.226.46900380.31634426.53900380.38634414-02-202218:10:0018:25:00conditions CloudyMedium1Snackmotorcycle2YesMetropolitian(min) 42
455860x4e21BANGRES16DEL03284.913.02919877.57099713.05919877.60099730-03-202221:55:0022:00:00conditions SandstormsJam1Mealscooter1NoMetropolitian(min) 29
455870x1178RANCHIRES16DEL01354.223.37129285.32787223.48129285.43787208-03-202221:45:0021:55:00conditions WindyJam2Drinksmotorcycle1NoMetropolitian(min) 33
455880x7c09JAPRES04DEL01304.826.90232875.79425726.91232875.80425724-03-202211:35:0011:45:00conditions WindyHigh1Mealmotorcycle0NoMetropolitian(min) 32
455890xd641AGRRES16DEL01214.60.0000000.0000000.0700000.07000016-02-202219:55:0020:10:00conditions WindyJam0Buffetmotorcycle1NoMetropolitian(min) 36
455900x4f8dCHENRES08DEL03304.913.02239480.24243913.05239480.27243911-03-202223:50:0000:05:00conditions CloudyLow1Drinksscooter0NoMetropolitian(min) 16
455910x5eeeCOIMBRES11DEL01204.711.00175376.98624111.04175377.02624107-03-202213:35:0013:40:00conditions CloudyHigh0Snackmotorcycle1NoMetropolitian(min) 26
455920x5fb2RANCHIRES09DEL02234.923.35105885.32573123.43105885.40573102-03-202217:10:0017:15:00conditions FogMedium2Snackscooter1NoMetropolitian(min) 36